Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/17142
Title: Antigenic diversity of dengue virus: implications for vaccine design
Authors: MOHAMMAD ASIF KHAN
Keywords: Immunology, Bioinformatics, Immunomics, Vaccine, Dengue virus, Flavivirus
Issue Date: 24-Sep-2009
Source: MOHAMMAD ASIF KHAN (2009-09-24). Antigenic diversity of dengue virus: implications for vaccine design. ScholarBank@NUS Repository.
Abstract: Antigenic diversity of viruses is a significant obstacle to the development of effective therapeutic and prophylactic vaccines. Mapping T-cell epitopes among highly variable viral variants and analysing their antigenic diversity presents us with a unique opportunity to improve our understanding of immune responses to viruses and help identify peptide targets for vaccine formulation. This thesis presents a novel bioinformatics approach focusing on systematic analyses of antigenic diversity in dengue virus (DENV) sequences. Large-scale antigenic diversity analyses presented in this thesis a) provides evidence that there are limited number of antigenic combinations in protein sequence variants of a viral species and b) suggests that a selection of short, highly conserved sequence fragments of viral proteome that also include promiscuous T-cell epitopes, applicable at the human population level, are sufficient to cover antigenic diversity of complete viral proteomes (such fragments will be referred to as <i>PE</i> for brevity). The most important contribution of this thesis is that it provided the first, comprehensive identification and characterization of DENV <i>PE</i>s. Forty-four, highly conserved DENV <i>PE</i>s were identified and the majority was found to be immune-relevant by their correspondence to both known and putative promiscuous T-cell epitopes. Thus, these DENV <i>PE</i>s represent good targets for the development of vaccines and further experimental validation. We defined the criteria for <i>PE</i>s, in the context of viral diversity, and developed the novel combination of bioinformatics and experimental approaches for their identification and characterization. The approach enables the design of a pipeline for large-scale systematic analysis of <i>PE</i>s within any other pathogen. The pipeline provides an experimental basis for the design of peptide-based vaccines that are targeted to both the majority of the genetic variants of the pathogen, and the majority of human population. The generic nature and usefulness of the approach to other flaviviruses was demonstrated through the application of the pipeline to West Nile virus (WNV), which also enabled comparative analysis of characteristics of <i>PE</i>s between DENV and WNV. Such comparative analysis across pathogens of interest may provide insights into the design of better vaccine strategies. An interesting and important finding made in this study was that there are significant differences in the conservation patterns between proteome/protein and the <i>PE</i> sites of flaviviruses, and that the patterns varied between <i>PE</i> sites, despite the flaviviruses sharing common ancestral origin, genomic architecture, and functional/structural roles of their proteins. This suggests that <i>PE</i>s may not be suitable for the formulation of a pan-<i>Flavivirus</i> vaccine and that vaccines need to be developed specific to each <i>Flavivirus</i>, preferentially using species-specific <i>PE</i>s. This thesis provides important insights into antigenic diversity and represents a seminal contribution to the field of dengue immunoinformatics, still in its infancy. The methodology pipeline offers a paradigm shift for the field of reverse vaccinology as it enables systematic screening of all known pathogen data for <i>PE</i>s and includes multiple additional criteria for assessment of their conservation ? a departure from the traditional approach where only a single or a small number of strains are studied with limited analyses of conservation.
URI: http://scholarbank.nus.edu.sg/handle/10635/17142
Appears in Collections:Ph.D Theses (Open)

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